Spark_RDD_SyedAcademy

Aug 1, 20170 likes132 views

The document discusses Apache Spark resilient distributed datasets (RDDs), which are distributed collections of objects that can be operated on in parallel across a cluster; it explains that writing your own RDD can help understand Spark's internal mechanics and is reasonable when connecting to external storage. RDDs allow data to be cached in memory and rebuilt if lost via lineage graphs defining their transformations, improving fault tolerance and performance.

Apache Spark
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Writing my own RDD? What for?
● To write your own RDD, you need to understand to
some extent internal mechanics of Apache Spark
● Writing your own RDD will prove you understand them
well
● When connecting to external storage, it is reasonable to
create your own RDD for it

RDD - the definition
RDD stands for resilient distributed dataset

RDD - the definition
RDD stands for resilient distributed dataset
Dataset - initial data comes from some
distributed storage

RDD - the definition
RDD stands for resilient distributed dataset
Dataset - initial data comes from some
distributed storage
Distributed - stored in nodes
among the cluster

Quiz: what is an “RDD”?
A: distributed collection of objects on disk
B: distributed collection of objects in memory
C: distributed collection of objects in Cassandra
Answer: could be any of the above!

Scientific Answer: RDD is an
Interface!
1. Set of partitions (“splits” in Hadoop)
2. List of dependencies on parent RDDs
3. Function to compute a partition (as
an Iterator) given its parent(s)
4. (Optional) partitioner (hash, range)
5. (Optional) preferred location(s)
for each partition
“lineage”
optimized
execution

RDD Persistence
•Each node stores any partitions of it that it computes in memory and
reuses them in other actions on that dataset.
•After marking an RDD to be persisted, the first time the dataset is
computed in an action, it will be kept in memory on the nodes.
•Allows future actions to be much faster (often by more than 10x) since
you’re not re-computing some data every time you perform an action.
•If data is too big to be cached, then it will spill to disk and memory will
gradually degrade
•Least Recently Used (LRU) replacement policy

RDD Persistence APIs
rdd.persist()
rdd.persist(StorageLevel)
•Persist this RDD with the default storage level (MEMORY_ONLY).
•You can override the StorageLevel for fine grain control over
persistence
rdd.cache()
•Persists the RDD with the default storage level (MEMORY_ONLY)
rdd.checkpoint()
•RDD will be saved to a file inside the checkpoint directory set with
SparkContext#setCheckpointDir(“/path/to/dir”)
•Used for RDDs with long lineage chains with wide dependencies since
it would be expensive to re-compute
rdd.unpersist()
•Marks it as non-persistent and/or removes all blocks of it from memory
and disk

Fault Tolerance
• RDDs contain lineage graphs (coarse grained updates/transformations) to
help it rebuild partitions that were lost
• Only the lost partitions of an RDD need to be recomputed upon failure.
• They can be recomputed in parallel on different nodes without having to roll
back the entire app
• Also lets a system tolerate slow nodes (stragglers) by running a backup
copy of the troubled task.
• Original process on straggling node will be killed when new process is
complete
• Cached/Check pointed partitions are also used to re-compute lost partitions
if available in shared memory

Thank you!
www.syedacademy.com
mail.syed786@gmail.com
info.syedacademy@gmail.com
+91-9030477368

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It's time you stopped letting your telemetry data pressure your budgets and get in the way of solving issues with agility! No more I say! Take back control of your telemetry data as we guide you through the open source project Fluent Bit. Learn how to manage your telemetry data from source to destination using the pipeline phases covering collection, parsing, aggregation, transformation, and forwarding from any source to any destination. Buckle up for a fun ride as you learn by exploring how telemetry pipelines work, how to set up your first pipeline, and exploring several common use cases that Fluent Bit helps solve. All this backed by a self-paced, hands-on workshop that attendees can pursue at home after this session (https://o11y-workshops.gitlab.io/workshop-fluentbit).

Automation Techniques in RPA - UiPath CertificateVICTOR MAESTRE RAMIREZ

Adobe Marketo Engage Champion Deep Dive - SFDC CRM Synch V2 & Usage DashboardsBradBedford3

Join Ajay Sarpal and Miray Vu to learn about key Marketo Engage enhancements. Discover improved in-app Salesforce CRM connector statistics for easy monitoring of sync health and throughput. Explore new Salesforce CRM Synch Dashboards providing up-to-date insights into weekly activity usage, thresholds, and limits with drill-down capabilities. Learn about proactive notifications for both Salesforce CRM sync and product usage overages. Get an update on improved Salesforce CRM synch scale and reliability coming in Q2 2025. Key Takeaways: Improved Salesforce CRM User Experience: Learn how self-service visibility enhances satisfaction. Utilize Salesforce CRM Synch Dashboards: Explore real-time weekly activity data. Monitor Performance Against Limits: See threshold limits for each product level. Get Usage Over-Limit Alerts: Receive notifications for exceeding thresholds. Learn About Improved Salesforce CRM Scale: Understand upcoming cloud-based incremental sync.

Secure Test Infrastructure: The Backbone of Trustworthy Software DevelopmentShubham Joshi

Exceptional Behaviors: How Frequently Are They Tested? (AST 2025)Andre Hora

Exceptions allow developers to handle error cases expected to occur infrequently. Ideally, good test suites should test both normal and exceptional behaviors to catch more bugs and avoid regressions. While current research analyzes exceptions that propagate to tests, it does not explore other exceptions that do not reach the tests. In this paper, we provide an empirical study to explore how frequently exceptional behaviors are tested in real-world systems. We consider both exceptions that propagate to tests and the ones that do not reach the tests. For this purpose, we run an instrumented version of test suites, monitor their execution, and collect information about the exceptions raised at runtime. We analyze the test suites of 25 Python systems, covering 5,372 executed methods, 17.9M calls, and 1.4M raised exceptions. We find that 21.4% of the executed methods do raise exceptions at runtime. In methods that raise exceptions, on the median, 1 in 10 calls exercise exceptional behaviors. Close to 80% of the methods that raise exceptions do so infrequently, but about 20% raise exceptions more frequently. Finally, we provide implications for researchers and practitioners. We suggest developing novel tools to support exercising exceptional behaviors and refactoring expensive try/except blocks. We also call attention to the fact that exception-raising behaviors are not necessarily “abnormal” or rare.

LEARN SEO AND INCREASE YOUR KNOWLDGE IN SOFTWARE INDUSTRYNidaFarooq10

Societal challenges of AI: biases, multilinguism and sustainabilityJordi Cabot

Exploring Wayland: A Modern Display Server for the FutureICS

Get & Download Wondershare Filmora Crack Latest [2025]saniaaftab72555

Copy & Past Link 👉👉 https://dr-up-community.info/ Wondershare Filmora is a video editing software and app designed for both beginners and experienced users. It's known for its user-friendly interface, drag-and-drop functionality, and a wide range of tools and features for creating and editing videos. Filmora is available on Windows, macOS, iOS (iPhone/iPad), and Android platforms.

Meet the Agents: How AI Is Learning to Think, Plan, and CollaborateMaxim Salnikov

TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...Andre Hora

Unittest and pytest are the most popular testing frameworks in Python. Overall, pytest provides some advantages, including simpler assertion, reuse of fixtures, and interoperability. Due to such benefits, multiple projects in the Python ecosystem have migrated from unittest to pytest. To facilitate the migration, pytest can also run unittest tests, thus, the migration can happen gradually over time. However, the migration can be timeconsuming and take a long time to conclude. In this context, projects would benefit from automated solutions to support the migration process. In this paper, we propose TestMigrationsInPy, a dataset of test migrations from unittest to pytest. TestMigrationsInPy contains 923 real-world migrations performed by developers. Future research proposing novel solutions to migrate frameworks in Python can rely on TestMigrationsInPy as a ground truth. Moreover, as TestMigrationsInPy includes information about the migration type (e.g., changes in assertions or fixtures), our dataset enables novel solutions to be verified effectively, for instance, from simpler assertion migrations to more complex fixture migrations. TestMigrationsInPy is publicly available at: https://github.com/altinoalvesjunior/TestMigrationsInPy.

Revolutionizing Residential Wi-Fi PPT.pptxnidhisingh691197